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An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection

KaewTraKulPong, P and Bowden, R (2002) An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection In: Video-Based Surveillance Systems. Springer US, New York. ISBN 978-1-4613-5301-0

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Real-time segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated visual surveillance, human-machine interface, and very low-bandwidth telecommunications. A typical method is background subtraction. Many background models have been introduced to deal with different problems. One of the successful solutions to these problems is to use a multi-colour background model per pixel proposed by Grimson et al [1, 2,3]. However, the method suffers from slow learning at the beginning, especially in busy environments. In addition, it can not distinguish between moving shadows and moving objects. This paper presents a method which improves this adaptive background mixture model. By reinvestigating the update equations, we utilise different equations at different phases. This allows our system learn faster and more accurately as well as adapts effectively to changing environment. A shadow detection scheme is also introduced in this paper. It is based on a computational colour space that makes use of our background model. A comparison has been made between the two algorithms. The results show the speed of learning and the accuracy of the model using our update algorithm over the Grimson et al’s tracker. When incorporate with the shadow detection, our method results in far better segmentation than The Thirteenth Conference on Uncertainty in Artificial Intelligence that of Grimson et al.

Item Type: Book Section
Subjects : Computing
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors : KaewTraKulPong, P and Bowden, R
Editors :
Remagnino, P
Jones, GA
Paragios, N
Regazzoni, CS
Date : 2002
DOI : 10.1007/978-1-4615-0913-4_11
Copyright Disclaimer : © 2002 Springer Science+Business Media
Uncontrolled Keywords : Background Subtraction, Shadow Suppression, Expectation-Maximisation Algorithm, Gaussian Mixture Model, Tracking, Motion detection, Compression, Surveillance
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 27 Oct 2016 11:05
Last Modified : 06 Jul 2019 05:16

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